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A Compendium to Ensure Computational Reproducibility in High-Dimensional Classification Tasks

Author

Listed:
  • Ruschhaupt Markus

    (German Cancer Research Centre)

  • Huber Wolfgang

    (German Cancer Research Center, Heidelberg, Germany)

  • Poustka Annemarie

    (German Cancer Research Centre)

  • Mansmann Ulrich

    (University of Heidelberg)

Abstract

We demonstrate a concept and implementation of a compendium for the classification of high-dimensional data from microarray gene expression profiles. A compendium is an interactive document that bundles primary data, statistical processing methods, figures, and derived data together with the textual documentation and conclusions. Interactivity allows the reader to modify and extend these components. We address the following questions: how much does the discriminatory power of a classifier depend on the choice of the algorithm that was used to identify it; what alternative classifiers could be used just as well; how robust is the result. The answers to these questions are essential prerequisites for validation and biological interpretation of the classifiers. We show how to use this approach by looking at these questions for a specific breast cancer microarray data set that first has been studied by Huang et al. (2003).

Suggested Citation

  • Ruschhaupt Markus & Huber Wolfgang & Poustka Annemarie & Mansmann Ulrich, 2004. "A Compendium to Ensure Computational Reproducibility in High-Dimensional Classification Tasks," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 3(1), pages 1-26, December.
    • Handle: RePEc:bpj:sagmbi:v:3:y:2004:i:1:n:37
    DOI: 10.2202/1544-6115.1078
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    Cited by:

    1. Boulesteix Anne-Laure, 2006. "Reader's Reaction to "Dimension Reduction for Classification with Gene Expression Microarray Data" by Dai et al (2006)," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 5(1), pages 1-7, June.
    2. Hans Kestler & Ludwig Lausser & Wolfgang Lindner & Günther Palm, 2011. "On the fusion of threshold classifiers for categorization and dimensionality reduction," Computational Statistics, Springer, vol. 26(2), pages 321-340, June.

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